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Cited by 156Law, AI, and Intellectual PropertyDigitalization, Law, and RegulationIntellectual Property Rights and Media

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Good models borrow, great models steal: intellectual property rights and generative AI

Open access
Chesterman, Simon·2025·Policy and Society
DOI 10.1093/polsoc/puae006
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Abstract

Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners of the data that are “scraped” (lawfully or unlawfully, with or without permission) should be compensated for that use. The second question revolves around the ownership of the output generated by AI, which is continually improving in quality and scale. These topics fall in the realm of intellectual property, a legal framework designed to incentivize and reward only human creativity and innovation. For some years, however, Britain has maintained a distinct category for “computer-generated” outputs; on the input issue, the EU and Singapore have recently introduced exceptions allowing for text and data mining or computational data analysis of existing works. This article explores the broader implications of these policy choices, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy, which might be rendered unsustainable. Lessons may be found in the music industry, which also went through a period of unrestrained piracy in the early digital era, epitomized by the rise and fall of the file-sharing service Napster. Similar litigation and legislation may help navigate the present uncertainty, along with an emerging market for “legitimate” models that respect the copyright of humans and are clear about the provenance of their own creations.

Overview

Integrity signals

Registry

Crossref · 10.1093/polsoc/puae006

Cites

56 of 56 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 49 references

Research works printed but not linked: 48

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Cited by

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Related

Metadata

Authors
Chesterman, S.
Affiliations
National University of Singapore (SG)
Corresponding author
Chesterman, Simon
ORCID
Chesterman, Simon
Venue
Policy and Society
Publisher
Oxford University Press (OUP)
Published
2024-02-12
Volume
44
Issue
1
Pages
23-37
ISSN
  • 1449-4035 (ISSN-L)
  • 1839-3373
DOI
10.1093/polsoc/puae006
OpenAlex
W4391754120
Cited by
156
References
49
Citations by year
2026: 60 · 2025: 66 · 2024: 27
FWCI
16.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Policy and Society (publishedVersion, cc-by)
  • SSRN Electronic Journal (submittedVersion)
  • National University of Singapore (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
Ministry of Education, Singapore (MOE AcRF TIER 3 Grant (MOE-MOET32022-0001))
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Law › Intellectual Property Rights and Media
Keywords
generative AI, intellectual property, copyright, training data, data scraping, computer-generated works, text and data mining exception, knowledge economy, creative industries, music industry, Napster, copyright litigation

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.